Device clustering

ABSTRACT

Clustering a plurality of client devices running an application as a function of a data structure such that the plurality of client devices are each assigned a cluster. Client devices having similar performance metrics are assigned the same cluster. An operation of the application is modified as a function of the performance metrics of the client device. The modification of application operation is performed by turning certain features of the application on and off using a rule based on device cluster.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. application Ser. No.16/398,426 filed on Apr. 30, 2019, the contents of which areincorporated fully herein by reference.

TECHNICAL FIELD

The present disclosure generally relates to performance metrics of anapplication operable on various client devices.

BACKGROUND

Performance metrics of an application, conventionally referred to as anapp, that runs on a client device vary from device to device. There arecurrently over 25K client devices operable on the Android® platform, andover 60 client devices operable on the iOS® platform. The performance ofthe application can vary based on a plurality of parameters includingboth hardware and software.

Latency is one of the notable metrics and can adversely affect theperformance of an application to the point where the application is slowor even unresponsive which impacts the user's engagement.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. Some examples are illustrated by way of example, and notlimitation, in the figures of the accompanying drawings in which:

FIG. 1 is a block diagram showing an example of clustering clientdevices having similar performance metrics system over a network;

FIG. 2 is a block diagram illustrating a clustering system operablewithin a server system;

FIG. 3 is a block diagram illustrating the performance engine operableon the client device;

FIG. 4 is an example of data structure comprising a device list showingthe top 5 client devices in each cluster 0 through 5 based on WAU;

FIG. 5 illustrates a data structure comprising a performance metricchart showing average performance metrics for client devices of eachcluster 0 through 5;

FIG. 6 illustrates an example algorithm of the clustering systemexecuted by instructions running in a processor;

FIG. 7 illustrates an example algorithm of the performance engine;

FIG. 8 is a high-level functional block diagram of an example clientdevice comprising a mobile device that communicates via network withserver system;

FIG. 9 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples; and

FIG. 10 is block diagram showing a software architecture within whichthe present disclosure may be implemented, in accordance with examples.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program productsillustrative of examples of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of various examplesof the disclosed subject matter. It will be evident, however, to thoseskilled in the art, that examples of the disclosed subject matter may bepracticed without these specific details. In general, well-knowninstruction instances, protocols, structures, and techniques are notnecessarily shown in detail.

One aspect of the present disclosure describes a system for landscapingvarious client devices by clustering client devices into clusters basedon similar performance metrics. In an example, the cluster can rangefrom 0 to 5, where a cluster 0 includes the least performant devices andcluster 5 includes flagship devices. The clustering is an off-lineprocess that is performed using actual client devices during workloadswhen a user engages an application running on a client device. Theperformance metrics are functional in that they relate to a user usingand engaging the application running on the client device, and theperformance metrics do not depend on a network. The performance metricsare stored and dynamically updated in a server system based onaggregated performance data. Performance data of the application isaggregated over time for each of the client devices, and lists rankingthe various client devices are maintained at a server system. Charts arealso maintained that include the performance metrics of client devices.New client devices that come available are combined with a clusteringlist based on how close their performance metrics are to the centroid ofclusters. The top client devices are based on WAU and WNU, and some arebased on expert opinion.

For example, performance characteristics, in order of importance, caninclude elapsed time from tapping on the app icon to being able tocapture an image (cold/warm), camera creation delay (image/video), crashrate, image capture to preview delay, camera recording delay, lensinitiation delay, and lens swipe latency. For latency metrics, the 90percentile (p90) is selected and data from the first 3 days after launchmay be excluded because of performance metric instability.

The clustering helps understand the effects of features/launches onAndroid device tiers and iOS tiers, provides a better track ofperformance metrics, helps focus engineering resources on the rightclient devices, provide feature gating, and get a morediverse/representative phones for quality assurance (QA).

Example uses of clustering is performing data analysis on featurereleases. For instance, when new features are released, the clusteringcan determine how the feature changes user engagement of the clientdevice in each device cluster. Clustering also helps improve a userexperience.

Another example use of clustering is to analyze performance metrics overtime, such as a year, and between upgrades. For example, the latency forusing certain features of an application on a client device is analyzed.Since users are always upgrading their phones to higher end devices,issues in performance metrics might be masked by this automaticimprovement in performance metrics for users who upgrade. So isimportant to monitor the performance metrics on each cluster over timeto make sure new features have not made the app slower for users whohave been using the same device over time.

Another example use of clustering is turning certain features on and offin clusters. An example is determining a maps feature takes too muchpower in client devices of cluster 0 and 1. Using a rule, the mapsfeature on a client device is not enabled when using an application ondevices of cluster 0 and 1 and remains enabled on devices of clusters2-5.

Another example is to aggregate performance metrics to eliminate uniquephones that are usually in the low-power mode.

Details of the clustering is provided below.

FIG. 1 is a block diagram illustrating a system 100, according to someexamples, configured to automatically perform clustering of clientdevices operable by a user. The system 100 includes one or more clientdevices such as client device 110. The client device 110 includes, butis not limited to, a mobile phone, desktop computer, laptop, portabledigital assistants (PDA), smart phone, tablet, ultrabook, netbook,laptop, multi-processor system, microprocessor-based or programmableconsumer electronic, game console, set-top box, computer in a vehicle,or any other communication device that a user may utilize to access thesystem 100. In some examples, the client device 110 includes a displaymodule (not shown) to display information (e.g., in the form of userinterfaces). In further examples, the client device 110 includes one ormore of touch screens, accelerometers, gyroscopes, cameras, microphones,global positioning system (GPS) devices, and so forth. The client device110 may be a device of a user that is used to access and utilize anonline social platform.

For example, client device 110 is a device of a given user who uses anapplication 114 on an online social platform. Client device 110 accessesa website of an online social platform hosted by a server system 108.The user inputs login credentials associated with the user. Serversystem 108 receives the request and provides access to the online socialplatform.

A user of the client device 110 launches and engages an application 114hosted by the server system 108. The client device 110 has a performanceengine 116 including a MicroKernel in client code performing theobservation or calculation of performance metrics on the client device110. The performance engine 116 downloads the performance metrics to theserver system 108 without significantly affecting operation of theapplication 114 and are used to perform clustering of the client device.

One or more users may be a person, a machine, or other means ofinteracting with the client device 110. In examples, the user may not bepart of the system 100 but may interact with the system 100 via theclient device 110 or other means. For instance, the user may provideinput (e.g., touch screen input or alphanumeric input) to the clientdevice 110 and the input may be communicated to other entities in thesystem 100 (e.g., third-party servers 130, server system 108, etc.) viathe network 102. In this instance, the other entities in the system 100,in response to receiving the input from the user, may communicateinformation to the client device 110 via the network 102 to be presentedto the user. In this way, the user interacts with the various entitiesin the system 100 using the client device 110.

The system 100 further includes a network 102. One or more portions ofnetwork 102 may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), a portion of the Internet, a portion ofthe public switched telephone network (PSTN), a cellular telephonenetwork, a wireless network, a WiFi network, a 4G LTE network, anothertype of network, or a combination of two or more such networks.

The client device 110 may access the various data and applicationsprovided by other entities in the system 100 via web client 112 (e.g., abrowser) or one or more client applications 114. The client device 110may include one or more client application(s) 114 (also referred to as“apps”) such as, but not limited to, a web browser, messagingapplication, electronic mail (email) application, an e-commerce siteapplication, a mapping or location application, an online home buyingand selling application, a real estate application, and the like.

In some examples, one or more client application(s) 114 are included ina given one of the client device 110, and configured to locally providethe user interface and at least some of the functionalities, with theclient application(s) 114 configured to communicate with other entitiesin the system 100 (e.g., third-party server(s) 128, server system 108,etc.), on an as-needed basis, for data processing capabilities notlocally available (e.g., to access location information, to authenticatea user, etc.). Conversely, one or more client application(s) 114 may notbe included in the client device 110, and then the client device 110 mayuse its web browser to access the one or more applications hosted onother entities in the system 100 (e.g., third-party server(s) 128,server system 108, etc.).

A server system 108 provides server-side functionality via the network102 (e.g., the Internet or wide area network (WAN)) to: one or morethird-party server(s) 128, and one or more client devices 110. Theserver system 108 includes an application server 104 including anapplication program interface (API) server 120, a web server 122, and aclustering system 124, that may be communicatively coupled with one ormore database(s) 126. The one or more database(s) 126 may be storagedevices that store data related to users of the server system 108,applications associated with the server system 108, cloud services,housing market data, and so forth. The one or more database(s) 126 mayfurther store information related to third-party server(s) 128,third-party application(s) 130, client device 110, client application(s)114, users, and so forth. In one example, the one or more database(s)126 may be cloud-based storage.

The server system 108 may be a cloud computing environment, according tosome examples. The server system 108, and any servers associated withthe server system 108, may be associated with a cloud-based application,in one example.

The server system 108 includes a clustering system 124. Clusteringsystem 124 may include one or more servers and may be associated with acloud-based application. Clustering system 124 obtains performancemetrics associated with operating application 114 on the client device110 from performance engine 116. The details of the clustering system124 are provided below in connection with FIG. 2, and the details of theperformance engine are provided below in connection with FIG. 3.

The system 100 further includes one or more third-party server(s) 128.The one or more third-party server(s) 128 may include one or morethird-party application(s) 130. The one or more third-partyapplication(s) 130, executing on third-party server(s) 128 may interactwith the server system 108 via API server 120 via a programmaticinterface provided by the API server 120. For example, one or more thethird-party applications 132 may request and utilize information fromthe server system 108 via the API server 120 to support one or morefeatures or functions on a website hosted by the third-party or anapplication hosted by the third-party. The third-party application(s)130, for example, may provide software version analysis functionalitythat is supported by relevant functionality and data in the serversystem 108.

FIG. 2 is a block diagram illustrating the clustering system 124operable within server system 108. Clustering system 124 is seen tocomprise data structures including a device list 202 associating variousclient devices 110 to a cluster, and a chart 204 illustrating theperformance metrics of each client device 110. The device list 202 andchart 204 are stored in memory 904 (FIG. 9). Devices are scored byapplying Principal Component Analysis model to the performance metrics.First component of the model is used to score and rank the devices.Cluster boundaries are formed by first dividing the set into 5 same sizegroup and then adjust based on expert's knowledge on the phoneperformance. Then cluster centroids are defined based on median valuesof performance metrics for devices in the same cluster and as new phonescome to the market, they get assigned to the cluster that they are mostclosed to. When a new generation of flagship phones come into themarket, which has a lot higher performance than cluster 5 centroids(high performant phones), a new set of clusters are defined toaccommodate the new generation of phones.

For example, performance characteristics, in order of importance, caninclude elapsed time from tapping on the icon until capturing animage/video (cold/warm), camera creation delay (image/video), crashrate, image capture to preview delay, camera recording delay, and lensinitiation delay. For latency metrics, the 90 percentile (p90) isselected and data from the first 3 days after launch may be excludedbecause of performance metric instability.

FIG. 3 is a block diagram illustrating the performance engine 116operable on the client device 110. The performance engine 116 is seen tocomprise a performance metric tracker 302 dynamically trackingperformance metrics 308 of the client device 110, such as using aMicroKernal, a performance metric communicator 304 using a client deviceprocessor/CPU (FIG. 8) communicating the performance metrics 308 to theclustering system 124. Client device 110 also has feature gating 306that gates the operation of certain features of application 114 as afunction of instructions sent by the clustering system 124 processingthe performance metrics of client device 110.

Referring to FIG. 4, in an example, device list 202 shows the top 5client devices in each cluster based on WAU. In another example, acomplete list 202 shows all client devices based on a given platform,such as Android and iOS, associated with a cluster.

FIG. 5 illustrates chart 204 showing average performance metrics forclient devices 110 of each cluster 0 through 5. Chart 204 illustratesthe number of weekly active users (WAU) and the number of weekly newusers (WNU). This chart 204 also illustrates client devices 110 that areconsidered unclassified.

FIG. 6 illustrates an example algorithm 50 of the clustering system 124executed by instructions 908 running in the processor 902 of serversystem 108 (FIG. 9).

At block 52, the processor 902 receives performance metrics of a firstclient device 110 that is running application 114. The performanceengine 116 of the first client device 110 dynamically monitorsperformance metrics of first client device 110 and wirelessly downloadsthe performance metrics via network 102 to the clustering system 124operating in server system 108. The performance metrics are stored inmemory 904.

At block 54, the processor 902 receives performance metrics of a secondclient device 110 that is running application 114. The performanceengine 116 of the second client device 110 dynamically monitorsperformance metrics of second client device 110 and wirelessly downloadsthe performance metrics via network 102 to the clustering system 124operating in server system 108. The performance metrics are stored inmemory 904.

At block 56, this process continues for N client devices runningapplication 114 and reporting performance metrics to the clusteringsystem 124 via network 102. The clustering system 124 stores theperformance metrics in memory 904.

At block 58, the downloaded performance metrics from each of the clientdevices 110 are then processed by the clustering system 124 to createdata structures, such as the list shown in FIG. 4 and the chart shown inFIG. 5.

At block 60, the clustering system 124 assigns a cluster to each clientdevice 110 based on its reported performance metrics, as shown in FIG.4, where the client devices 110 with similar performance metrics areassigned the same cluster. As shown in FIG. 4, the top 5 devices foreach cluster are listed. In another example, all client devices 110 arelisted along with their assigned cluster. A list for client devices 110based on the Android® platform are included in a first list, and allclient devices 110 based on the iOS® platform are stored in a differentsecond list.

At block 62, the clustering system 124 modifies the operation of theapplication 114 on certain client devices 110 using feature gating 210based on the performance metrics. Feature gating 210 ensures a featureis supported on a client device 110, and also that the feature willperform well on the client device 110.

An example use of feature gating of application 114 is turning certainfeatures on and off in clusters. An example is determining a mapsfeature of the application 114 takes too much power in client devices ofclusters 0 and 1. Using a rule, the maps feature of the application 114is not enabled when using an application on devices of cluster 0 and 1and remains enabled on devices of clusters 2-5.

The clustering helps understand the effects of features/launches onAndroid device tiers and iOS tiers, provides a better track ofperformance metrics, helps focus engineering resources on the rightclient devices, provide feature gating, and get a morediverse/representative phones for quality assurance (QA).

Example uses of clustering is performing data analysis on featurereleases. For instance, when new features are released, the clusteringcan determine how the feature changes user engagement of the clientdevice. Clustering also helps improve a user experience.

Another example use of clustering is to analyze performance metrics overtime, such as a year, and between upgrades.

FIG. 7 illustrates an example algorithm 70 of the performance engine 116executed by instructions running in the processor/central processingunit (CPU) 830 of client device 110 (FIG. 8).

At block 72, the processor 830 of client device 110 monitors performancemetrics 308 of client device 110 that is running application 114. Theperformance engine 116 of the client device 110 dynamically monitorsperformance metrics 308 of client device 110 and stores them in memory840A.

At block 74, the processor 830 wirelessly downloads the performancemetrics 308 via network 102 to the clustering system 124 operating inserver system 108. The processor 830 dynamically downloads theperformance metrics 308, such as in real time in one example, and inblocks of data in another example. The clustering system 124 processesthe performance metrics as previously described, such as to clusterclient devices 110 and create gating instructions for each of the clientdevices 110.

At block 76, the performance engine 116 receives gating instructionsfrom clustering system 124 via network 102.

At block 78, the client device 110 gates certain features of application114 as a function of the received gating instructions. An example ofgating features is turning certain features on and off in buckets. Oneexample is the clustering system 124 determining a maps feature takestoo many resources in client devices 110 of cluster 0 and 1. Using arule, the maps feature on a client device 110 is not enabled when usingapplication 114 on devices of cluster 0 and 1.

Another example of gating is to aggregate performance metrics toeliminate unique client devices that are usually in the low-power mode.

FIG. 8 is a high-level functional block diagram of an example clientdevice 110 comprising a mobile device that communicates via network 102with server system 108 of FIG. 9. Shown are elements of a touch screentype mobile device 890 having the performance engine 116, although othernon-touch type mobile devices can be used under consideration here.Examples of touch screen type mobile devices that may be used include(but are not limited to) a smart phone, a personal digital assistant(PDA), a tablet computer, a laptop computer, or other portable device.However, the structure and operation of the touch screen type devices isprovided by way of example, and the subject technology as describedherein is not intended to be limited thereto. For purposes of thisdiscussion, FIG. 8 therefore provides a block diagram illustration ofthe example mobile device 110 having a touch screen display fordisplaying content and receiving user input as (or as part of) the userinterface. Mobile device 890 also includes a camera(s) 870, such asvisible light camera(s).

The activities that are the focus of discussions here involve monitoringand reporting of performance metrics and gating features of application114 running on the mobile phone 110. As shown in FIG. 8, the mobiledevice 110 includes at least one digital transceiver (XCVR) 810, shownas WWAN XCVRs, for digital wireless communications via a wide areawireless mobile communication network 102. The mobile device 110 alsoincludes additional digital or analog transceivers, such as short rangeXCVRs 820 for short-range network communication, such as via NFC, VLC,DECT, ZigBee, Bluetooth™, or WiFi. For example, short range XCVRs 820may take the form of any available two-way wireless local area network(WLAN) transceiver of a type that is compatible with one or morestandard protocols of communication implemented in wireless local areanetworks, such as one of the Wi-Fi standards under IEEE 802.11 and 4GLTE.

To generate location coordinates for positioning of the mobile device890, the mobile device 890 can include a global positioning system (GPS)receiver. Alternatively, or additionally the mobile device 110 canutilize either or both the short range XCVRs 820 and WWAN XCVRs 810 forgenerating location coordinates for positioning. For example, cellularnetwork, WiFi, or Bluetooth™ based positioning systems can generate veryaccurate location coordinates, particularly when used in combination.Such location coordinates can be transmitted to the eyewear device overone or more network connections via XCVRs 820.

The transceivers 810, 820 (network communication interface) conforms toone or more of the various digital wireless communication standardsutilized by modern mobile networks. Examples of WWAN transceivers 810include (but are not limited to) transceivers configured to operate inaccordance with Code Division Multiple Access (CDMA) and 3rd GenerationPartnership Project (3GPP) network technologies including, for exampleand without limitation, 3GPP type 2 (or 3GPP2) and LTE, at timesreferred to as “4G.” For example, the transceivers 810, 820 providetwo-way wireless communication of information including digitized audiosignals, still image and video signals, web page information for displayas well as web related inputs, and various types of mobile messagecommunications to/from the mobile device 110 for user identificationstrategies.

Several of these types of communications through the transceivers 810,820 and a network, as discussed previously, relate to protocols andprocedures in support of communications with the server system 108 forperforming performance metric monitoring and gating. Suchcommunications, for example, may transport packet data via the shortrange XCVRs 820 over the wireless connections of network 102 to and fromthe server system 108 as shown in FIG. 1. Such communications, forexample, may also transport data utilizing IP packet data transport viathe WWAN XCVRs 810 over the network (e.g., Internet) 102 shown inFIG. 1. Both WWAN XCVRs 810 and short range XCVRs 820 connect throughradio frequency (RF) send-and-receive amplifiers (not shown) to anassociated antenna (not shown).

The mobile device 110 further includes a microprocessor 830, shown as aCPU, sometimes referred to herein as the host controller. A processor isa circuit having elements structured and arranged to perform one or moreprocessing functions, typically various data processing functions.Although discrete logic components could be used, the examples utilizecomponents forming a programmable CPU. A microprocessor for exampleincludes one or more integrated circuit (IC) chips incorporating theelectronic elements to perform the functions of the CPU. The processor830, for example, may be based on any known or available microprocessorarchitecture, such as a Reduced Instruction Set Computing (RISC) usingan ARM architecture, as commonly used today in mobile devices and otherportable electronic devices. Of course, other processor circuitry may beused to form the CPU 830 or processor hardware in smartphone, laptopcomputer, and tablet.

The microprocessor 830 serves as a programmable host controller for themobile device 110 by configuring the mobile device to perform variousoperations, for example, in accordance with instructions or programmingexecutable by processor 830. For example, such operations may includevarious general operations of the mobile device, as well as operationsrelated to performance metric monitoring, reporting to server system108, and gating. Although a processor may be configured by use ofhardwired logic, typical processors in mobile devices are generalprocessing circuits configured by execution of programming.

The mobile device 110 includes a memory or storage device system, forstoring data and programming. In the example, the memory system mayinclude a flash memory 840A and a random access memory (RAM) 840B. TheRAM 840B serves as short term storage for instructions and data beinghandled by the processor 830, e.g., as a working data processing memory.The flash memory 840A typically provides longer term storage.

Hence, in the example of mobile device 110, the flash memory 840A isused to store programming or instructions for execution by the processor830. Depending on the type of device, the mobile device 110 stores andruns a mobile operating system through which specific applications,including application 114. Applications, such as the performance metricmonitoring and gating, may be a native application, a hybridapplication, or a web application (e.g., a dynamic web page executed bya web browser) that runs on mobile device 890 to uniquely identify theuser. Examples of mobile operating systems include Google Android®,Apple iOS® (I-Phone or iPad devices), Windows Mobile®, Amazon Fire OS®,RIM BlackBerry® operating system, or the like.

As shown, flash memory 840A storage device stores a database ofperformance metrics 308. The database of performance metrics 308 isaccumulated over time as different a user runs application 114. Theflash memory 840A also stores gating information of the client device110, including which features are enabled and unenabled based on theperformance metrics.

FIG. 9 is a diagrammatic representation of the machine 900 within whichinstructions 908 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 900 to performany one or more of the methodologies discussed herein may be executed.For example, the instructions 908 may cause the machine 900 to executeany one or more of the methods described herein. The instructions 908transform the general, non-programmed machine 900 into a particularmachine 900 programmed to carry out the described and illustratedfunctions in the manner described. The machine 900 may operate as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 900 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 900 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), aPDA, an entertainment media system, a cellular telephone, a smart phone,a mobile device, a wearable device (e.g., a smart watch), a smart homedevice (e.g., a smart appliance), other smart devices, a web appliance,a network router, a network switch, a network bridge, or any machinecapable of executing the instructions 908, sequentially or otherwise,that specify actions to be taken by the machine 900. Further, while onlya single machine 900 is illustrated, the term “machine” shall also betaken to include a collection of machines that individually or jointlyexecute the instructions 908 to perform any one or more of themethodologies discussed herein.

The machine 900 may include processors 902, memory 904, and I/Ocomponents 942, which may be configured to communicate with each othervia a bus 944. In an example, the processors 902 (e.g., a CentralProcessing Unit (CPU), a Reduced Instruction Set Computing (RISC)processor, a Complex Instruction Set Computing (CISC) processor, aGraphics Processing Unit (GPU), a Digital Signal Processor (DSP), anASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, orany suitable combination thereof) may include, for example, a processor906 and a processor 910 that execute the instructions 908. The term“processor” is intended to include multi-core processors that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.9 shows multiple processors 902, the machine 900 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core processor), multiple processors with a single core,multiple processors with multiples cores, or any combination thereof.

The memory 904 includes a main memory 912, a static memory 914, and astorage unit 916, both accessible to the processors 902 via the bus 944.The main memory 904, the static memory 914, and storage unit 916 storethe instructions 908 embodying any one or more of the methodologies orfunctions described herein. The instructions 908 may also reside,completely or partially, within the main memory 912, within the staticmemory 914, within machine-readable medium 918 (e.g., a non-transitorymachine-readable storage medium) within the storage unit 916, within atleast one of the processors 902 (e.g., within the processor's cachememory), or any suitable combination thereof, during execution thereofby the machine 900.

Furthermore, the machine-readable medium 918 is non-transitory (in otherwords, not having any transitory signals) in that it does not embody apropagating signal. However, labeling the machine-readable medium 918“non-transitory” should not be construed to mean that the medium isincapable of movement; the medium should be considered as beingtransportable from one physical location to another. Additionally, sincethe machine-readable medium 918 is tangible, the medium may be amachine-readable device.

The I/O components 942 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 942 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 942 mayinclude many other components that are not shown in FIG. 9. In variousexamples, the I/O components 942 may include output components 928 andinput components 930. The output components 928 may include visualcomponents (e.g., a display such as a plasma display panel (PDP), alight emitting diode (LED) display, a liquid crystal display (LCD), aprojector, or a cathode ray tube (CRT)), acoustic components (e.g.,speakers), haptic components (e.g., a vibratory motor, resistancemechanisms), other signal generators, and so forth. The input components930 may include alphanumeric input components (e.g., a keyboard, a touchscreen configured to receive alphanumeric input, a photo-opticalkeyboard, or other alphanumeric input components), point-based inputcomponents (e.g., a mouse, a touchpad, a trackball, a joystick, a motionsensor, or another pointing instrument), tactile input components (e.g.,a physical button, a touch screen that provides location, force oftouches or touch gestures, or other tactile input components), audioinput components (e.g., a microphone), and the like.

In further examples, the I/O components 942 may include biometriccomponents 932, motion components 934, environmental components 936, orposition components 938, among a wide array of other components. Forexample, the biometric components 932 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 934 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope), and so forth. The environmental components936 include, for example, illumination sensor components (e.g.,photometer), temperature sensor components (e.g., one or morethermometers that detect ambient temperature), humidity sensorcomponents, pressure sensor components (e.g., barometer), acousticsensor components (e.g., one or more microphones that detect backgroundnoise), proximity sensor components (e.g., infrared sensors that detectnearby objects), gas sensors (e.g., gas detection sensors to detectionconcentrations of hazardous gases for safety or to measure pollutants inthe atmosphere), or other components that may provide indications,measurements, or signals corresponding to a surrounding physicalenvironment. The position components 938 include location sensorcomponents (e.g., a GPS receiver component), altitude sensor components(e.g., altimeters or barometers that detect air pressure from whichaltitude may be derived), orientation sensor components (e.g.,magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 942 further include communication components 940operable to couple the machine 900 to network 102 and client devices 110via a coupling 924 and a coupling 926, respectively. For example, thecommunication components 940 may include a network interface componentor another suitable device to interface with the network 102. In furtherexamples, the communication components 940 may include wiredcommunication components, wireless communication components, cellularcommunication components, Near Field Communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 110 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 940 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 940 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components940, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., memory 904, main memory 912, static memory914, memory of the processors 902), storage unit 916 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 908), when executedby processors 902, cause various operations to implement the disclosedexamples.

The instructions 908 may be transmitted or received over the network102, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components940) and using any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions908 may be transmitted or received using a transmission medium via thecoupling 926 (e.g., a peer-to-peer coupling) to the devices 110.

FIG. 10 is a block diagram 1000 illustrating a software architecture1004, which can be installed on any one or more of the devices describedherein. The software architecture 1004 is supported by hardware such asa machine 1002 that includes processors 1020, memory 1026, and I/Ocomponents 1038. In this example, the software architecture 1004 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 1004 includes layerssuch as an operating system 1012, libraries 1010, frameworks 1008, andapplications 1006. Operationally, the applications 1006 invoke API calls1050 through the software stack and receive messages 1052 in response tothe API calls 1050.

The operating system 1012 manages hardware resources and provides commonservices. The operating system 1012 includes, for example, a kernel1014, services 1016, and drivers 1022. The kernel 1014 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 1014 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1016 canprovide other common services for the other software layers. The drivers1022 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1022 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., Universal Serial Bus(USB) drivers), WI-FI® drivers, audio drivers, power management drivers,and so forth.

The libraries 1010 provide a low-level common infrastructure used by theapplications 1006. The libraries 1010 can include system libraries 1018(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1010 can include APIlibraries 1024 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 1010 can also include a widevariety of other libraries 1028 to provide many other APIs to theapplications 1006.

The frameworks 1008 provide a high-level common infrastructure that isused by the applications 1006. For example, the frameworks 1008 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 1008 canprovide a broad spectrum of other APIs that can be used by theapplications 1006, some of which may be specific to a particularoperating system or platform.

In an example, the applications 1006 may include a home application1036, a contacts application 1030, a browser application 1032, a bookreader application 1034, a location application 1042, a mediaapplication 1044, a messaging application 1046, a game application 1048,and a broad assortment of other applications such as a third-partyapplication 1040. The e applications 1006 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 1006, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the third-partyapplication 1040 (e.g., an application developed using the ANDROID™ orIOS™ software development kit (SDK) by an entity other than the vendorof the particular platform) may be mobile software running on a mobileoperating system such as IOS™, ANDROID™, WINDOWS® Phone, or anothermobile operating system. In this example, the third-party application1040 can invoke the API calls 1050 provided by the operating system 1012to facilitate functionality described herein.

The terms and expressions used herein are understood to have theordinary meaning as is accorded to such terms and expressions withrespect to their corresponding respective areas of inquiry and studyexcept where specific meanings have otherwise been set forth herein.Relational terms such as first and second and the like may be usedsolely to distinguish one entity or action from another withoutnecessarily requiring or implying any actual such relationship or orderbetween such entities or actions. The terms “comprises,” “comprising,”“includes,” “including,” or any other variation thereof, are intended tocover a non-exclusive inclusion, such that a process, method, article,or apparatus that comprises or includes a list of elements or steps doesnot include only those elements or steps but may include other elementsor steps not expressly listed or inherent to such process, method,article, or apparatus. An element preceded by “a” or “an” does not,without further constraints, preclude the existence of additionalidentical elements in the process, method, article, or apparatus thatcomprises the element.

In addition, in the foregoing Detailed Description, it can be seen thatvarious features are grouped together in various examples for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the claimed examplesrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, the subject matter to be protected liesin less than all features of any single disclosed example. Thus, thefollowing claims are hereby incorporated into the Detailed Description,with each claim standing on its own as a separately claimed subjectmatter.

The examples illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other examples may be used and derived therefrom, such that structuraland logical substitutions and changes may be made without departing fromthe scope of this disclosure. The Detailed Description, therefore, isnot to be taken in a limiting sense, and the scope of various examplesis defined only by the appended claims, along with the full range ofequivalents to which such claims are entitled.

What is claimed is:
 1. A method, comprising: receiving, by a processor,performance metrics from mobile devices running an application having afeature; aggregating, by the processor, the received performance metricsas a function of the mobile devices and their associated performancemetrics; clustering, by the processor, the mobile devices such that themobile devices are each assigned to a cluster, wherein the mobiledevices having similar performance metrics are assigned to the samecluster; storing in a data table the assigned clusters, and the similarperformance metrics of the mobile devices; and directing, by theprocessor, an implementation of the feature of the application based onthe data table.
 2. The method of claim 1, wherein the clustering isdynamically updated.
 3. The method of claim 1, wherein the performancemetrics of the mobile devices are a function of running the feature. 4.The method of claim 1, wherein the mobile devices have an attribute, andthe mobile devices are clustered based on the attribute.
 5. The methodof claim 4, wherein the attribute is comprised of an operating platformor a type of mobile device.
 6. The method of claim 4, wherein theattribute is a model of the mobile devices.
 7. The method of claim 1,wherein a first group of the mobile devices having similar performancemetrics and a first attribute is assigned a first cluster, wherein asecond group of the mobile devices having similar performance metricsand a second attribute is assigned a second cluster, and wherein a thirdgroup of the mobile devices having similar performance metrics and athird attribute is assigned a third cluster.
 8. The method of claim 7,wherein the first cluster includes the mobile devices with the lowestperformance metrics, the third cluster includes the mobile devices withthe best performance metrics, and the second cluster includes the mobiledevices with performance metrics between the performance metrics of thefirst group and the third group of mobile devices.
 9. The method ofclaim 1, wherein the processor instructs the mobile devices to gate anoperation of the feature without closing the application.
 10. The methodof claim 1, wherein the data table comprises average performance metricsof the mobile devices assigned the same cluster.
 11. A system,comprising: a memory that stores instructions; and a processor thatexecutes the instructions to perform operations comprising: receivingperformance metrics from mobile devices running an application having afeature; aggregating the received performance metrics as a function ofthe mobile devices and their associated performance metrics; clusteringthe mobile devices such that the mobile devices are each assigned to acluster, wherein the mobile devices having similar performance metricsare assigned to the same cluster; storing in a data table the assignedclusters, and the similar performance metrics of the mobile devices; anddirecting an implementation of the feature of the application based onthe data table.
 12. The system of claim 11, wherein the clustering isdynamically updated.
 13. The system of claim 11, wherein the performancemetrics of the mobile devices are a function of running the feature. 14.The system of claim 11, wherein the mobile devices have an attribute,and the mobile devices are clustered based on the attribute.
 15. Thesystem of claim 14, wherein the attribute is comprised of an operatingplatform or a type of mobile device.
 16. The system of claim 11, whereinthe processor is configured to instruct the mobile devices to gate anoperation of the feature without closing the application.
 17. The systemof claim 11, wherein the data table comprises average performancemetrics of the mobile devices assigned the same cluster.
 18. Anon-transitory processor-readable storage medium storingprocessor-executable instructions that, when executed by a processor ofa machine, cause the processor to perform operations comprising:receiving performance metrics from mobile devices running an applicationhaving a feature; aggregating the received performance metrics as afunction of the mobile devices and their associated performance metrics;clustering the mobile devices such that the mobile devices are eachassigned to a cluster, wherein the mobile devices having similarperformance metrics are assigned to the same cluster; storing in a datatable the assigned clusters, and the similar performance metrics of themobile devices; and directing an implementation of the feature of theapplication based on the data table.
 19. The non-transitoryprocessor-readable storage medium of claim 18, wherein the instructionscause the processor to perform an operation to dynamically update theclusters.
 20. The non-transitory processor-readable storage medium ofclaim 18, wherein the performance metrics of the mobile devices are afunction of running the feature.